{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:51:08Z","timestamp":1773093068930,"version":"3.50.1"},"reference-count":37,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T00:00:00Z","timestamp":1572912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,11,5]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence the performance of financial markets within a short period of time. The purpose of this paper is to investigate how incidents affect stock prices and volatility using vector error correction and autoregressive-generalized auto regressive conditional Heteroskedasticity models, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>To characterize the investors\u2019 responses to incidents, the authors introduce indices derived using search volumes from Google Trends and the Baidu Index.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The empirical results indicate that an outbreak of disasters can increase volatility temporarily, and exert significant negative effects on stock prices in a relatively long time. In addition, indices derived from different search engines show differentiation, with the Google Trends search index mainly representing international investors and appearing more significant and persistent.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study contributes to the existing literature by incorporating open-source data to analyze how catastrophic events affect financial markets and effect persistence.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/imds-03-2019-0190","type":"journal-article","created":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T04:26:50Z","timestamp":1573014410000},"page":"350-365","source":"Crossref","is-referenced-by-count":29,"title":["Using Google Trends and Baidu Index to analyze the impacts of disaster events on company stock prices"],"prefix":"10.1108","volume":"120","author":[{"given":"Ying","family":"Liu","sequence":"first","affiliation":[]},{"given":"Geng","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Lanyi","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jichang","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Qingqing","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"1","key":"key2020012215045776300_ref001","first-page":"33","article-title":"Investor attention and stock market volatility","volume":"28","year":"2014","journal-title":"The Review of Financial Studies"},{"issue":"4","key":"key2020012215045776300_ref002","doi-asserted-by":"crossref","first-page":"619","DOI":"10.2307\/253474","article-title":"Catastrophic shocks in the property-liability insurance industry: evidence on regulatory and contagion effects","volume":"63","year":"1996","journal-title":"The Journal of Risk and Insurance"},{"issue":"1","key":"key2020012215045776300_ref003","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0304-4076(95)01749-6","article-title":"Fractionally integrated generalized autoregressive conditional heteroskedasticity","volume":"74","year":"1996","journal-title":"Journal of Econometrics"},{"issue":"4","key":"key2020012215045776300_ref004","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/j.1540-6288.1987.tb01258.x","article-title":"The adjustment of stock prices to completely unanticipated events","volume":"22","year":"1987","journal-title":"The Financial Review"},{"issue":"1","key":"key2020012215045776300_ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter mood predicts the stock market","volume":"2","year":"2011","journal-title":"Journal of Computational Science"},{"issue":"7","key":"key2020012215045776300_ref006","article-title":"Web search queries can predict stock market volumes","volume":"7","year":"2012","journal-title":"PloS One"},{"key":"key2020012215045776300_ref007","article-title":"The impact of natural disasters on the stock returns and volatilities of local firms","year":"2016","journal-title":"The Quarterly Review of Economics and Finance"},{"issue":"349","key":"key2020012215045776300_ref008","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1080\/01621459.1975.10480264","article-title":"Intervention analysis with applications to economic and environmental problems","volume":"70","year":"1975","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"key2020012215045776300_ref009","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1108\/SEF-01-2015-0036","article-title":"Classifying Chinese bull and bear markets: indices and individual stocks","volume":"33","year":"2016","journal-title":"Studies in Economics and Finance"},{"issue":"S1","key":"key2020012215045776300_ref010","first-page":"2","article-title":"Predicting the present with Google trends","volume":"88","year":"2009","journal-title":"Economic Record"},{"issue":"5","key":"key2020012215045776300_ref011","first-page":"1461","article-title":"In search of attention","volume":"LXVI","year":"2011","journal-title":"Journal of Finance"},{"key":"key2020012215045776300_ref012","volume-title":"Descartes\u2019 Error: Emotion, Reason, and the Human Brain","year":"1994"},{"key":"key2020012215045776300_ref013","doi-asserted-by":"crossref","unstructured":"Gao, L., Li, O. and Yeung, E. 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